How YouTube Serves As The Content Engine Of The Internet’s Dark Side

How YouTube Serves As The Content Engine Of The Internet’s Dark Side

Everyone knows that Twitter and Facebook spread bad information and hate speech. But YouTube, which pays for conspiracy theories seen by millions, may be even worse.

David Seaman is the Pizzagate King of the Internet.  On
Twitter, Seaman posts dozens of messages a day to his 66,000
followers, often about the secret cabal — including
Rothschilds, Satanists, and the other nabobs of the New World
Order — behind the nation’s best-known, super-duper-secret
child sex ring under a DC pizza parlor.  But it’s on YouTube
where he really goes to work. Since Nov. 4, four days before
the election, Seaman has uploaded 136 videos, more than one a
day. Of those, at least 42 are about Pizzagate. The videos,
which tend to run about eight to fifteen minutes, typically
consist of Seaman, a young, brown-haired man with glasses and
a short beard, speaking directly into a camera in front of a
white wall. He doesn’t equivocate: Recent videos are titled
“Pizzagate Will Dominate 2017, Because It Is Real” and
“#PizzaGate New Info 12/6/16: Link To Pagan God of
Pedophilia/Rape.”  Seaman has more than 150,000 subscribers.
His videos, usually preceded by preroll ads for major brands
like Quaker Oats and Uber, have been watched almost 18 million
times, which is roughly the number of people who tuned in to
last year’s season finale of NCIS, the most popular show on
television.

Defense Against the Dark Arts: Networked Propaganda and Counter-Propaganda
February 24, 2017  Author Jonathan Stray

In honor of MisinfoCon this weekend, it’s time for a brain dump on propaganda — that is, getting large numbers of people to believe something for political gain. Many of my journalist and technologist colleagues have started to think about propaganda in the wake of the US election, and related issues like “fake news” and organized trolling. My goal here is to connect this new wave of enthusiasm to history and research.

This post is about persuasion. I’m not going to spend much time on the ethics of these techniques, and even less on the question of who is actually right on any particular point. That’s for another conversation. Instead, I want to talk about what works. All of these methods are just tools, and some are more just than others.

Think of this as Defense Against the Dark Arts.
http://jonathanstray.com/networked-propaganda-and-counter-propaganda

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The Matrix is here

Google Unveils Neural Network with “Superhuman” Ability to Determine the
Location of Almost Any Image

Guessing the location of a randomly chosen Street View image is hard, even
for well-traveled humans. But Google’s latest artificial-intelligence
machine manages it with relative ease.
By Emerging Technology from the arXiv
Feb 24 2017

https://www.technologyreview.com/s/600889/google-unveils-neural-network-with-superhuman-ability-to-determine-the-location-of-almost/

Here’s a tricky task. Pick a photograph from the Web at random. Now try to
work out where it was taken using only the image itself. If the image shows
a famous building or landmark, such as the Eiffel Tower or Niagara Falls,
the task is straightforward. But the job becomes significantly harder when
the image lacks specific location cues or is taken indoors or shows a pet
or food or some other detail.

Nevertheless, humans are surprisingly good at this task. To help, they
bring to bear all kinds of knowledge about the world such as the type and
language of signs on display, the types of vegetation, architectural
styles, the direction of traffic, and so on. Humans spend a lifetime
picking up these kinds of geolocation cues.

So it’s easy to think that machines would struggle with this task. And
indeed, they have.

Today, that changes thanks to the work of Tobias Weyand, a computer vision
specialist at Google, and a couple of pals. These guys have trained a
deep-learning machine to work out the location of almost any photo using
only the pixels it contains.

Their new machine significantly outperforms humans and can even use a
clever trick to determine the location of indoor images and pictures of
specific things such as pets, food, and so on that have no location cues.

Their approach is straightforward, at least in the world of machine
learning. Weyand and co begin by dividing the world into a grid consisting
of over 26,000 squares of varying size that depend on the number of images
taken in that location.

So big cities, which are the subjects of many images, have a more
fine-grained grid structure than more remote regions where photographs are
less common. Indeed, the Google team ignored areas like oceans and the
polar regions, where few photographs have been taken.

Next, the team created a database of geolocated images from the Web and
used the location data to determine the grid square in which each image was
taken. This data set is huge, consisting of 126 million images along with
their accompanying Exif location data.

Weyand and co used 91 million of these images to teach a powerful neural
network to work out the grid location using only the image itself. Their
idea is to input an image into this neural net and get as the output a
particular grid location or a set of likely candidates.

They then validated the neural network using the remaining 34 million
images in the data set. Finally they tested the network—which they call
PlaNet—in a number of different ways to see how well it works.

The results make for interesting reading. To measure the accuracy of their
machine, they fed it 2.3 million geotagged images from Flickr to see
whether it could correctly determine their location. “PlaNet is able to
localize 3.6 percent of the images at street-level accuracy and 10.1
percent at city-level accuracy,” say Weyand and co. What’s more, the
machine determines the country of origin in a further 28.4 percent of the
photos and the continent in 48.0 percent of them.

That’s pretty good. But to show just how good, Weyand and co put PlaNet
through its paces in a test against 10 well-traveled humans. For the test,
they used an online game that presents a player with a random view taken
from Google Street View and asks him or her to pinpoint its location on a
map of the world. [snip]

A warning from Bill Gates, Elon Musk, and Stephen Hawking
By Quincy Larson
Feb 19 2017

https://medium.freecodecamp.com/bill-gates-and-elon-musk-just-warned-us-about-the-one-thing-politicians-are-too-scared-to-talk-8db9815fd398

“The automation of factories has already decimated jobs in traditional
manufacturing, and the rise of artificial intelligence is likely to extend
this job destruction deep into the middle classes, with only the most
caring, creative or supervisory roles remaining.” — Stephen Hawking
There’s a rising chorus of concern about how quickly robots are taking away
human jobs.

Here’s Elon Musk on Thursday at the the World Government Summit in Dubai:

“What to do about mass unemployment? This is going to be a massive social
challenge. There will be fewer and fewer jobs that a robot cannot do better
[than a human]. These are not things that I wish will happen. These are
simply things that I think probably will happen.” — Elon Musk
And today Bill Gates proposed that governments start taxing robot workers
the same way we tax human workers:

“You cross the threshold of job-replacement of certain activities all sort
of at once. So, you know, warehouse work, driving, room cleanup, there’s
quite a few things that are meaningful job categories that, certainly in
the next 20 years [will go away].” — Bill Gates
Jobs are vanishing much faster than anyone ever imagined.

In 2013, policy makers largely ignored two Oxford economists who suggested
that 45% of all US jobs could be automated away within the next 20 years.
But today that sounds all but inevitable.

Transportation and warehousing employ 5 million Americans

Those self-driving cars you keep hearing about are about to replace a lot
of human workers.

Currently in the US, there are:

• 600,000 Uber drivers
• 181,000 taxi drivers
• 168,000 transit bus drivers
• 505,000 school bus drivers

There’s also around 1 million truck drivers in the US. And Uber just bought
a self-driving truck company.

As self driving cars become legal in more states, we’ll see a rapid
automation of all of these driving jobs. If a one-time $30,000 truck
retrofit can replace a $40,000 per year human trucker, there will soon be a
million truckers out of work.

And it’s not just the drivers being replaced. Soon entire warehouses will
be fully automated.

I strongly recommend you invest 3 minutes in watching this video. It shows
how a fleet of small robots can replace a huge number of human warehouse
workers.

There are still some humans working in those warehouses, but it’s only a
matter of time before some sort of automated system replaces them, too.

8 million Americans work as retail salespeople and cashiers.

Many of these jobs will soon be automated away.

Amazon is testing a type of store with virtually no employees. You just
walk in, grab what you want, and walk out.

[snip]

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Women’s History Month The True Story of ‘Hidden Figures’

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The True Story of ‘Hidden Figures’ and the Women Who Crunched the Numbers for NASA

SEE Katherine Johnson at the oscars 2017

CHANGING GIRLS’ ATTITUDES ABOUT COMPUTERS

Some highlights:

“Across the United States and Canada, universities and colleges are facing a significant increase in enrollment in both undergraduate computer science (CS) courses and programs. The current enrollment surge has exceeded previous CS booms, and there is a general sense that the current growth in enrollment is substantially different than that of the mid-1980s and late 1990s.”

“The enrollment growth in the mid-1980s is sometimes referred to as the “PC boom” and the enrollment growth in the late 1990s is sometimes referred to as the “dot-com boom.” CRA Snowbird Conference attendees suggest that we are currently in “Generation CS,” where CS enrollment across the nation is surging due to the pervasiveness of computing in today’s society. Computing plays a significant role in daily life, and students with interests in a variety of fields are beginning to understand that training in computer science is vital.”

“Many units face increased faculty retention problems, are not able to hire teaching faculty into newly created teaching positions, and realize that there are not enough new PhDs to fill open faculty slots in the targeted areas.”

“A positive consequence of the current enrollment surge is a significant increase in the number of women and underrepresented minority (URM) students in computer science, both in courses and as majors. In addition, there is also some good news in regard to the percentage of women and URM students in aggregate; the good news, however, is not universal across all units1 surveyed.”

“Current pressures on computer science units are extremely difficult to manage and will also intensify if enrollments continue to grow. Institutional administrators need to work with computer science units to find sustainable approaches to meet the student demand…”

“Given the available data on job postings, Ph.D. production, and the insufficient number of new Ph.D.s pursuing academic positions, units may not be able to hire faculty members as planned. In addition, units may face increased faculty retention problems.”

 

CRA Releases Report on Surge in Computer Science Enrollments

Some highlights:

“Across the United States and Canada, universities and colleges are facing a significant increase in enrollment in both undergraduate computer science (CS) courses and programs. The current enrollment surge has exceeded previous CS booms, and there is a general sense that the current growth in enrollment is substantially different than that of the mid-1980s and late 1990s.”

“The enrollment growth in the mid-1980s is sometimes referred to as the “PC boom” and the enrollment growth in the late 1990s is sometimes referred to as the “dot-com boom.” CRA Snowbird Conference attendees suggest that we are currently in “Generation CS,” where CS enrollment across the nation is surging due to the pervasiveness of computing in today’s society. Computing plays a significant role in daily life, and students with interests in a variety of fields are beginning to understand that training in computer science is vital.”

“Many units face increased faculty retention problems, are not able to hire teaching faculty into newly created teaching positions, and realize that there are not enough new PhDs to fill open faculty slots in the targeted areas.”

“A positive consequence of the current enrollment surge is a significant increase in the number of women and underrepresented minority (URM) students in computer science, both in courses and as majors. In addition, there is also some good news in regard to the percentage of women and URM students in aggregate; the good news, however, is not universal across all units1 surveyed.”

“Current pressures on computer science units are extremely difficult to manage and will also intensify if enrollments continue to grow. Institutional administrators need to work with computer science units to find sustainable approaches to meet the student demand…”

“Given the available data on job postings, Ph.D. production, and the insufficient number of new Ph.D.s pursuing academic positions, units may not be able to hire faculty members as planned. In addition, units may face increased faculty retention problems.”

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Achtung! Germany hates Cayla and so should you.

Are you listening?

German regulators have banned an internet-connected doll called ‘My Friend Cayla’ that can chat with children, warning that it was a de facto “hidden spying device”.  Parents were urged to disable the interactive toy by the Federal Network Agency which enforces bans on surveillance devices.

Banned In Germany: Kids’ Doll Is Labeled An Espionage Device
Data privacy, and specifically related to what information is being collected from children through voice-enabled toys. NPR

Cayla looks like an everyday doll and gives no notice that it collects and transmits everything it hears — in this case, to a voice-recognition company in the U.S. whose other customers include intelligence agencies.

A doll called My Friend Cayla listens a little too well, according to German regulators who say the toy is essentially a stealthy espionage device that shares what it hears and is also vulnerable to takeover by third parties.  “Cayla ist verboten in Deutschland,” says Jochen Homann, the president of Germany’s Federal Network Agency (the Bundessnetzagentur), announcing a ban on the doll in Germany on Friday.

Cayla will also dial the Hitler phone and tell them what your parents are saying…..

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http://www.bbc.com/news/world-europe-39105852

Hitler Bunker Phone up For Auction in Maryland
http://www.newsmax.com

“This was Hitler’s personal instrument of death,” Ranulf added. “It is a very sinister piece of equipment, when you think about what it was used for. He would have used it extensively to scream brutal orders to those running the concentration camps and to his generals on the battlefield.”
Adolf Hitler’s bunker phone, which has been described as the Nazi leaders’ “mobile device of destruction,” will be sold this month at an auction in the U.S.  The phone, which has been held at an English country house since Hitler’s suicide in 1945, is set to be sold in Maryland by Alexander Historical Auctions on Feb. 19, according to the New York Post.  According to the auction house, Hitler’s phone, painted in red with his name engraved, was “arguably the most destructive ‘weapon’ of all time, which sent millions to their deaths around the world,” CNN noted.  Hitler was known to use the bunker telephone to give commands that led to the deaths of millions of Jews during World War II, according to a catalog for the auction house.  The “death phone” could go for more than $500,000 at the upcoming auction.

 

 

 

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Al Gore’s ‘Inconvenient Truth’? — A $30,000 Utility Bill

 

Armed with Gore’s utility bills for the last two years, the Tennessee Center for Policy Research charged Monday that the gas and electric bills for the former vice president’s 20-room home and pool house devoured nearly 221,000 kilowatt-hours in 2006, more than 20 times the national average of 10,656 kilowatt-hours.  “If this were any other person with $30,000-a-year in utility bills, I wouldn’t care,” says the Center’s 27-year-old president, Drew Johnson. “But he tells other people how to live and he’s not following his own rules.”

http://abcnews.go.com/Politics/GlobalWarming/story?id=2906888

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